Abstract
Gut microbiota has been reported to influence immune responses and various diseases. However, the causal relationship between specific bacterial taxa and IgG levels following H pylori infection remains unclear. We employed a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary statistics to evaluate the causal effects of gut microbiota composition on IgG levels post-H pylori infection. Robustness was assessed through sensitivity analyses, including MR-Egger, weighted median, and MR-PRESSO tests. Additionally, functional enrichment analysis of significant genes was performed to explore potential biological pathways. Our MR analysis identified 11 microbial taxa significantly associated with IgG levels. Six gut microbial taxa, including Akkermansia and Ruminococcaceae UCG002, exhibit a negative causal association with IgG level changes, exerting beneficial effects. Conversely, 5 taxa such as Parabacteroides and Dialister display a significant positive causal relationship with IgG level alterations, potentially inducing excessive activation or suppression of the immune system and leading to detrimental consequences. Sensitivity analyses confirmed the robustness of these findings with no evidence of pleiotropy or heterogeneity. Functional enrichment analysis highlighted pathways linked to immune regulation, neurotransmitter signaling, and inflammatory responses. This study elucidates the causal relationships between gut microbiota and IgG levels after H pylori infection, offering insights into microbiota-mediated immune modulation and potential targets for therapeutic intervention.
Keywords: gut microbiota, H pylori infection, IgG levels, immune modulation, Mendelian randomization
1. Introduction
Helicobacter pylori (H pylori) infection poses a significant public health challenge, characterized by high incidence and mortality rates. Epidemiological data indicate that H pylori infection affects 4.4 billion people annually,[1] and it has been identified as a Group I carcinogen by the International Agency for Research on Cancer and currently is considered a necessary but insufficient cause of gastric adenocarcinoma,[2] with a mortality rate of up to 11.0% in male and 4.9% in female.[3] The pathogenesis of H pylori infection is complex, often involving immune system evasion mechanisms and inflammatory responses. The bacteria are able to colonize the gastric mucosa by interacting with host receptors, such as mucin 5 (MUC5AC), and by forming biofilms to persist in the harsh acidic environment of the stomach. Inflammatory responses induced by H pylori can lead to chronic gastritis, peptic ulcers, and even gastric cancer in some individuals.[4,5] Additionally, H pylori has been shown to modulate the immune response, leading to the activation of pro-inflammatory cytokines like IL-8 and TNF-α, and the infection is associated with systemic humoral immune response reflected by raised serum levels of specific IgG.[6] Despite recent advancements in treatment strategies, including the use of antibiotics and proton pump inhibitors, eradication rates remain suboptimal. The increasing antibiotic resistance of H pylori strains, especially to clarithromycin and metronidazole, has complicated treatment outcomes, necessitating alternative approaches for effective eradication.[7,8] This highlights the urgent need for further research to explore the underlying mechanisms of immune evasion and antibiotic resistance in H pylori.
In recent years, studies have revealed that gut microbiota dysbiosis is closely associated with the development and progression of H pylori-related immune dysregulation and inflammation. Changes in gut microbiota composition can lead to disrupted immune homeostasis, such as impaired gut barrier function and altered IgG responses, which triggers chronic inflammatory responses and increases the risk of related diseases like gastric and colorectal cancers.[9] For instance, H pylori infection has been shown to induce significant changes in microbial diversity and abundance, promoting a pro-inflammatory environment that exacerbates the host’s immune response.[10,11] Several studies have confirmed the correlation between gut microbiota alterations and H pylori infection. For example, H pylori infection induces changes in microbial diversity, including reductions in beneficial taxa such as Firmicutes and increases in Proteobacteria. These shifts can modulate the immune response, potentially influencing IgG levels and the progression of gastric and extragastric diseases.[12] H pylori infection also alters immune functions through mechanisms involving TLRs (Toll-like receptors) and STING (stimulator of interferon genes) signaling, both of which are crucial for the regulation of immune responses, including antibody production like IgG.[13] Moreover, studies show that the gut microbiota may influence H pylori-related immune modulation through multiple mechanisms, such as the direct interaction with immune pathways that regulate inflammation. For instance, H pylori suppresses immune responses by downregulating STING, a nucleic acid sensor, which could affect IgG-mediated defense mechanisms.[14] Inflammation and immune responses can also be modulated by metabolites produced by gut microbiota, such as short-chain fatty acids (SCFAs), which play a crucial role in balancing pro-inflammatory and anti-inflammatory cytokines.[15] Dysbiosis caused by H pylori not only disrupts these beneficial metabolic pathways but also promotes a microbial signature that exacerbates chronic inflammation.[16] Furthermore, while the eradication of H pylori is essential for reducing gastric cancer risk, it can disrupt gut microbiota balance, leading to transient immune dysregulation.[17] The use of antibiotics and proton pump inhibitors during eradication therapy has been associated with temporary changes in microbial diversity and immune responses, which can impact long-term health.[18] Collectively, these findings suggest that gut microbiota dysbiosis plays a pivotal role in shaping the immune landscape during and after H pylori infection. By influencing immune pathways like TLR signaling, SCFA production, and microbial community structure, gut microbiota can directly impact IgG levels and inflammatory responses, highlighting its potential as a therapeutic target.
IgG update is a reliable and accurate test and can be used as a convenient screening test, thus serving as an alternative to endoscopy. Although previous studies have all emphasized the relationship between the intestinal flora and IgG levels after H pylori infection, the presence of different microbial strains may lead to differences in IgG detection results among different studies.[19] However, there are still significant knowledge gaps regarding the specific mechanisms driving these causal relationships. Key limitations of current research include: Incomplete understanding of gut microbiota composition and function, particularly its modulation by H pylori infection and eradication therapy.[20–22] Lack of large-scale, multicenter clinical data to establish generalizable findings regarding microbiota-mediated immune responses.[23,24] Limitations in research methodologies, including insufficient use of longitudinal and high-resolution multi-omic approaches, which fail to accurately elucidate the causal relationship between gut microbiota and IgG level modulation.[25,26]
To address the aforementioned research gaps, this study employs Mendelian randomization (MR) to explore the causal relationship between gut microbiota and IgG levels in response to H pylori infection. MR allows for a more accurate assessment of the microbiota’s specific impact on IgG levels by overcoming biases inherent in traditional observational studies. Additionally, gene colocalization analysis will be used to identify core genes associated with the relationship between microbiota and IgG levels, followed by GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to uncover the underlying molecular mechanisms. The aim of this study is to elucidate the role of gut microbiota in the immune response to H pylori infection, providing novel insights for the development of therapeutic strategies for H pylori infection.
2. Materials and methods
2.1. Study framework
This investigation was guided by the STROBE-MR standards[27]and adhered to the principles of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative.[28] The MR methodology is predicated on 3 assumptions[29]: the genetic variants used as instruments are associated exclusively with specified gut microbiota taxa; these variants are free from confounding factors related to IgG levels post-H pylori infection; the influence of these variants on IgG levels is mediated solely through their association with the microbiota. This analysis incorporated genome-wide association study (GWAS) summary statistics already available, negating the need for new data collection and ethical approval. The study research process is illustrated in Figure 1.
Figure 1.
Study research flowchart.
2.2. Data collection
Genomic data related to gut microbiota were sourced from the most recent GWAS compiled by the Mibiogen Consortium (https://mibiogen.gcc.rug.nl/menu/main/home/). This dataset encompassed information from 18,340 individuals across 24 different cohorts, predominantly of European ancestry (85%). Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays.[30] Utilizing 16S rRNA gene sequencing, we categorized the microbiota into 211 taxonomic units spanning 6 hierarchical levels: 9 phyla, 16 classes, 20 orders, 35 families, and 131 genera.[31] Our main outcome indicator is the IgG antibody level after H pylori infection (ieu-b-4905). The relevant data was extracted from the OpenGWAS (https://opengwas.io/datasets/ieu-b-4905) database, which contains a comprehensive, population-based cohort study of European adults. A total of 53,013 participants were included in the analysis of IgG levels, including those 4683 patients who was infected by H pylori. As a result, we obtained a sample dataset of 4683 cases with elevated and decreased IgG levels after infection. The selection of instrumental variables (IVs) for the MR study was rigorous: Significance threshold: each microbial taxonomic group was assessed at a significance threshold of P < 1.0 × 10–5 within its respective locus. Linkage disequilibrium (LD) calculation: LD was calculated using the 1000 Genomes European reference panel, retaining single nucleotide polymorphisms (SNPs) with an LD threshold of r2 < 0.01, prioritizing those with the lowest P-values. Effect allele frequency (EAF): Only SNPs with an EAF > 0.01 were included to ensure sufficient genetic variation. Exclusion criteria: palindromic SNPs were excluded, and any SNP with an F-statistic below 10 was omitted to avoid biases due to weak IVs.
2.3. MR statistical analysis
For our MR analysis, After a comparison of various tools, we selected the inverse variance weighting (IVW) method supplemented by MR-Egger, weighted median, simple mode, and weighted mode techniques to comprehensively evaluate the causal effects of gut microbiota on IgG levels post-H.. When the core assumptions are satisfied, IVW exhibits optimal statistical characteristics. Its calculation is straightforward, and the results can be readily interpreted as a weighted average. Moreover, it serves as a crucial benchmark and starting point for the analysis results. Meanwhile, the significance results, effect sizes, in conjunction with the consistency and heterogeneity test results of various sensitivity analyses, jointly constitute a comprehensive body of evidence in support of the causal relationship, pylori infection. Each SNP’s effect was assessed using the Wald ratio method. We applied Cochrane Q test to evaluate heterogeneity among SNP instruments, adopting a random-effects IVW model in cases of significant heterogeneity (P < .05) for a more conservative estimation.
The robustness of our estimates was further assessed using the weighted median test, effective when valid IVs constitute more than 50% of the analysis weight. The MR-Egger regression allowed for the assessment of pleiotropy. Sensitivity analyses included the MR-Egger intercept test, the global test for outliers (MR-PRESSO), and leave-one-out analysis to evaluate the impact of individual SNPs and detect any pleiotropic effects.
Statistical analyses were executed using R software (version 4.1.3), employing the “TwoSampleMR” and “MRPRESSO” packages for various MR methods and outlier testing, respectively. A significance level was maintained at P < .05 throughout the analyses.
2.4. Mapping SNPs to genes
We employed the gProfiler (https://biit.cs.ut.ee/gprofiler/snpense) platform to map each SNP to its closest gene, which could be overlapping or adjacent (upstream or downstream). Following this mapping, we proceeded with MR analysis on the IVs (SNPs) and the exposure genes, incorporating eQTL data sourced from the CAGE study to analyze gene expression at the transcript level in peripheral blood from 4683 individuals, primarily of European descent. Only eQTLs meeting a conditional P-value of < 0.05 were considered for further analysis.
2.5. Functional enrichment analysis of key genes
We analyzed the genes associated with both beneficial and harmful microbiota, employing an odds ratio (OR) >1 to indicate beneficial effects and <1 for harmful effects. The “clusterProfiler” R package facilitated the functional enrichment analysis, examining gene ontology (GO) categories and KEGG pathways. We visualized significant GO terms and pathways using the ‘ggplot2’ package, setting a significance threshold at P < .05.
3. Results
3.1. MR analysis
Our MR study employed between 3 to 22 SNPs as IVs for each microbial taxonomic group associated with IgG levels post-H pylori infection. The robustness of these instruments is indicated by F-statistics ranging from 17 to 29, signifying strong instruments with no evidence of weak instrument bias. Additionally, our MR-PRESSO global test confirmed the absence of significant pleiotropic effects across all analyses (P > .05).
Our inverse variance-weighted (IVW) MR analysis identified 11 taxonomic groups that exert a causal influence on the primary outcome, specifically the IgG levels following H pylori infection. Notable findings include: We found that the increased abundance of Eubacterium oxidoreducens group (OR, 0.80; 95% CI, 0.63–1.00; P = .048), Ruminococcaceae UCG002 (OR, 0.81; 95% CI, 0.66–0.98; P = .034), Anaerofilum (OR, 0.80; 95% CI, 0.67–0.97; P = .021), Akkermansia (OR, 0.70; 95% CI, 0.57–0.87; P = .001), Ruminococcaceae UCG011 (OR, 0.85; 95% CI, 0.74–0.99; P = .035), and Candidatus Soleaferrea (OR, 0.82; 95% CI, 0.70–0.95; P = .011) were positively associated with reduced IgG levels, suggesting positive causal effects. In contrast, we observed that Dialister (OR, 1.31; 95% CI, 1.02–1.68; P = .036), Parabacteroides (OR, 1.48; 95% CI, 1.07–2.06; P = .019), Lachnospira (OR, 1.78; 95% CI, 1.27–2.50; P = .001), Ruminococcus gauvreauii group (OR, 1.44; 95% CI, 1.11–1.87; P = .006), and Eubacterium brachy group (OR, 1.18; 95% CI, 1.02–1.36; P = .029) were associated with an increased risk of elevated IgG levels, indicating negative effects (see Tables 1 and 2, Fig. 2).
Table 1.
MR estimates for the association between gut microbiota and IgG levels after H pylori infection.
| Bacterial taxa (exposure) | MR Method | NSNP | OR | L 95% CI | U 95% CI | P-value |
|---|---|---|---|---|---|---|
| Eubacterium oxidoreducens group | MR-Egger | 5 | 0.61669 | 0.28598 | 1.32985 | .30541 |
| Weighted median | 5 | 0.82079 | 0.60537 | 1.11286 | .20355 | |
| IVW | 5 | 0.79535 | 0.63387 | 0.99796 | .04797 | |
| Simple mode | 5 | 0.81966 | 0.54806 | 1.22586 | .38770 | |
| Weighted mode | 5 | 0.81820 | 0.56527 | 1.18429 | .34749 | |
| Dialister | MR-Egger | 12 | 0.74819 | 0.25958 | 2.15651 | .60293 |
| Weighted median | 12 | 1.22872 | 0.90043 | 1.67671 | .19404 | |
| IVW | 12 | 1.30855 | 1.01750 | 1.68285 | .03616 | |
| Simple mode | 12 | 0.92539 | 0.53012 | 1.61539 | .79007 | |
| Weighted mode | 12 | 0.94050 | 0.53369 | 1.65742 | .83584 | |
| Ruminococcaceae UCG002 | MR-Egger | 22 | 0.71557 | 0.40842 | 1.25369 | .25585 |
| Weighted median | 22 | 0.75123 | 0.57906 | 0.97460 | .03126 | |
| IVW | 22 | 0.80585 | 0.65984 | 0.98416 | .03429 | |
| Simple mode | 22 | 0.59347 | 0.35023 | 1.00564 | .06604 | |
| Weighted mode | 22 | 0.63058 | 0.39105 | 1.01684 | .07243 | |
| Anaerofilum | MR-Egger | 7 | 0.60221 | 0.30064 | 1.20629 | .21188 |
| Weighted median | 7 | 0.78408 | 0.61692 | 0.99654 | .04677 | |
| IVW | 7 | 0.80316 | 0.66679 | 0.96742 | .02095 | |
| Simple mode | 7 | 0.76805 | 0.53558 | 1.10142 | .20135 | |
| Weighted mode | 7 | 0.76805 | 0.53107 | 1.11079 | .21052 | |
| Parabacteroides | MR-Egger | 8 | 0.76236 | 0.31020 | 1.87361 | .57581 |
| Weighted median | 8 | 1.57526 | 1.06327 | 2.33379 | .02346 | |
| IVW | 8 | 1.48195 | 1.06629 | 2.05965 | .01917 | |
| Simple mode | 8 | 1.83353 | 0.81873 | 4.10614 | .18403 | |
| Weighted mode | 8 | 1.76240 | 0.86619 | 3.58591 | .16188 | |
| Akkermansia | MR-Egger | 12 | 0.54882 | 0.25735 | 1.17041 | .15153 |
| Weighted median | 12 | 0.74750 | 0.56252 | 0.99331 | .04483 | |
| IVW | 12 | 0.70393 | 0.577100 | 0.86782 | .00101 | |
| Simple mode | 12 | 0.78347 | 0.48905 | 1.25516 | .33198 | |
| Weighted mode | 12 | 0.78007 | 0.49998 | 1.21708 | .29718 | |
| Lachnospira | MR-Egger | 7 | 1.38666 | 0.21034 | 9.14167 | .74786 |
| Weighted median | 7 | 1.63807 | 1.03827 | 2.58439 | .03389 | |
| IVW | 7 | 1.77892 | 1.26754 | 2.49662 | .00087 | |
| Simple mode | 7 | 1.56996 | 0.82329 | 2.99383 | .21987 | |
| Weighted mode | 7 | 1.55070 | 0.86648 | 2.77522 | .19005 | |
| Ruminococcaceae UCG011 | MR-Egger | 8 | 0.83678 | 0.38877 | 1.80106 | .66469 |
| Weighted median | 8 | 0.84324 | 0.70601 | 1.00714 | .05991 | |
| IVW | 8 | 0.85306 | 0.73572 | 0.98912 | .03530 | |
| Simple mode | 8 | 0.83568 | 0.64188 | 1.08799 | .22412 | |
| Weighted mode | 8 | 0.83755 | 0.64835 | 1.08197 | .21692 | |
| Candidatus Soleaferrea | MR-Egger | 13 | 0.65919 | 0.35752 | 1.21539 | .20882 |
| Weighted median | 13 | 0.76443 | 0.61645 | 0.94793 | .01440 | |
| IVW | 13 | 0.81721 | 0.69962 | 0.95457 | .01088 | |
| Simple mode | 13 | 0.72476 | 0.50232 | 1.04572 | .11090 | |
| Weighted mode | 13 | 0.73767 | 0.50652 | 1.07431 | .13866 | |
| Ruminococcus gauvreauii group | MR-Egger | 9 | 1.96073 | 0.63084 | 6.09421 | .28266 |
| Weighted median | 9 | 1.39716 | 1.00845 | 1.93570 | .04437 | |
| IVW | 9 | 1.44469 | 1.11314 | 1.87498 | .00568 | |
| Simple mode | 9 | 1.41191 | 0.87026 | 2.29067 | .19990 | |
| Weighted mode | 9 | 1.35053 | 0.83054 | 2.19608 | .26029 | |
| Eubacterium brachy group | MR-Egger | 9 | 1.12953 | 0.70702 | 1.80453 | .62602 |
| Weighted median | 9 | 1.08325 | 0.89144 | 1.31634 | .42125 | |
| IVW | 9 | 1.17675 | 1.01724 | 1.36127 | .02852 | |
| Simple mode | 9 | 1.03346 | 0.76889 | 1.38907 | .83278 | |
| Weighted mode | 9 | 1.03346 | 0.77121 | 1.38488 | .83108 |
MR = Mendelian randomization, NSNP = number of single nucleotide polymorphisms.
Table 2.
SNP for causal association between gut microbiota and IgG levels after H pylori infection.
| IF | SNP | EA | OA | BETA | EAF | Chr | Pos | SE | P-value | Size |
|---|---|---|---|---|---|---|---|---|---|---|
| Akkermansia | rs11184341 | G | C | 0.0655854786845953 | 0.2783 | 1 | 105,422,565 | 0.0142236403272038 | 4.06353315924574e−06 | 11,863 |
| rs111862613 | T | C | 0.0911199279099132 | 0.166 | 12 | 130,309,670 | 0.0196748154721001 | 3.3923575427861e−06 | 11,620 | |
| rs117107102 | A | G | 0.204406165609251 | 0.0447 | 18 | 49,473,635 | 0.0431628939124462 | 3.01138637202498e−06 | 4974 | |
| rs11729256 | T | C | 0.0750472529910655 | 0.2386 | 4 | 95,027,272 | 0.0150183954238514 | 6.58392581071756e−07 | 11,860 | |
| rs12908520 | G | A | 0.0617720233657728 | 0.4563 | 15 | 97,570,657 | 0.0130953899052071 | 2.26481559953004e−06 | 11,862 | |
| rs2602429 | C | T | 0.0745352408198936 | 0.2217 | 16 | 81,063,149 | 0.0156201269093276 | 2.7156911339784e−06 | 11,689 | |
| rs4242783 | G | A | 0.0685453812915544 | 0.2873 | 10 | 5,064,327 | 0.0147700621473626 | 2.99798631135938e−06 | 11,689 | |
| rs4936098 | A | G | 0.0649224919006123 | 0.34 | 11 | 130,280,667 | 0.013593380200573 | 1.10359417154571e−06 | 11,857 | |
| rs61779207 | G | A | −0.076053856 | 0.1779 | 1 | 41,074,472 | 0.0167769103490103 | 6.32115832246886e−06 | 11,590 | |
| rs74542928 | T | C | 0.112622737273028 | 0.0785 | 4 | 100,544,188 | 0.0236434376110673 | 1.48020245168224e−06 | 11,215 | |
| rs9349825 | A | G | −0.070340692 | 0.2793 | 6 | 56,341,481 | 0.0147132912731865 | 2.59885963475292e−06 | 11,857 | |
| rs941682 | G | A | −0.063295745 | 0.3111 | 20 | 31,867,840 | 0.0143776796764711 | 9.17126418056966e−06 | 11,808 | |
| Anaerofilum | rs10794359 | T | C | −0.095352431 | 0.4871 | 11 | 1,051,715 | 0.020059978674458 | 2.23495553740529e−06 | 4994 |
| rs17012738 | T | G | 0.090341715700164 | 0.4076 | 2 | 76,698,142 | 0.0200239426413777 | 7.24168057532088e−06 | 5051 | |
| rs17105491 | G | C | −0.19310744 | 0.0596 | 10 | 125,294,406 | 0.0411186602953347 | 1.56818918513841e−06 | 4856 | |
| rs4244069 | G | A | −0.146774518 | 0.1133 | 12 | 67,167,461 | 0.032659424039613 | 9.81202424527424e−06 | 5030 | |
| rs712981 | A | C | 0.100759616767355 | 0.4185 | 3 | 129,686,434 | 0.0202902476344993 | 6.83114949634712e−07 | 5051 | |
| rs79598899 | C | T | 0.182585804267907 | 0.0825 | 2 | 191,139,151 | 0.0357284469369788 | 3.74728054952477e−07 | 4709 | |
| rs9299345 | T | C | −0.136399548 | 0.1173 | 9 | 104,339,812 | 0.0302371634487277 | 8.04308968898995e−06 | 5051 | |
| Candidatus Soleaferrea | rs10090365 | A | G | −0.083441348 | 0.5586 | 8 | 138,638,199 | 0.0180988821805702 | 4.17309456261167e−06 | 6145 |
| rs10108780 | A | G | −0.09283404 | 0.2674 | 8 | 123,882,833 | 0.01998065961638 | 3.64079224872166e−06 | 5999 | |
| rs10809135 | T | C | 0.0834857903433733 | 0.4911 | 9 | 10,611,282 | 0.0182426302783199 | 5.47173960273462e−06 | 5999 | |
| rs11153159 | G | C | −0.128050003 | 0.1292 | 6 | 109,376,647 | 0.0285446105875536 | 4.41523481111354e−06 | 6101 | |
| rs2193878 | T | A | 0.228255106962276 | 0.0487 | 3 | 190,349,049 | 0.0509241346505462 | 9.45546462506185e−06 | 3088 | |
| rs36155147 | C | T | 0.104966126063328 | 0.3618 | 7 | 352,368 | 0.0240995270476851 | 5.41138273215946e−06 | 5958 | |
| rs4294381 | T | C | 0.11219652590834 | 0.1799 | 1 | 224,688,107 | 0.0231860071033086 | 1.36849860922471e−06 | 6145 | |
| rs4678258 | T | C | 0.0986136157619694 | 0.2763 | 3 | 137,945,192 | 0.0215646267746116 | 5.53182915152151e−06 | 6101 | |
| rs6489992 | A | G | −0.084043749 | 0.3847 | 12 | 115,352,769 | 0.018703073246532 | 7.88785595287559e−06 | 6142 | |
| rs6494306 | A | G | −0.09693036 | 0.1968 | 15 | 62,394,540 | 0.0214220864295107 | 5.8005088568536e−06 | 6145 | |
| rs7400877 | T | C | −0.095108487 | 0.2505 | 14 | 76,403,542 | 0.0212799068458365 | 9.29447973708365e−06 | 6145 | |
| rs830149 | C | G | 0.184646616431892 | 0.0586 | 19 | 47,991,441 | 0.0397237479208195 | 9.58382306411593e−06 | 5065 | |
| rs9973954 | A | G | 0.0892075136621146 | 0.3062 | 2 | 19,885,907 | 0.0195401488834511 | 5.9479605757517e−06 | 6145 | |
| Dialister | rs10138457 | T | C | −0.113053794 | 0.0716 | 14 | 102,458,052 | 0.0261932532464563 | 7.88121812298115e−06 | 10,968 |
| rs10938938 | G | A | −0.077351446 | 0.1938 | 4 | 23,294,359 | 0.0170914905075365 | 7.37062587862634e−06 | 11,401 | |
| rs11071887 | T | C | 0.0662403023312743 | 0.2634 | 15 | 32,955,095 | 0.0146314844082137 | 5.91406674324497e−06 | 11,989 | |
| rs11166701 | G | A | −0.065530102 | 0.5159 | 8 | 138,295,725 | 0.0131859995034347 | 5.50745911801279e−07 | 11,720 | |
| rs2314294 | T | C | 0.0865931921654296 | 0.1183 | 16 | 9,336,862 | 0.0193718948303736 | 8.07899033285776e−06 | 11,339 | |
| rs2435610 | A | C | 0.0647131491516663 | 0.2604 | 7 | 150,890,034 | 0.0143314239150829 | 5.93268612653093e−06 | 11,984 | |
| rs4747450 | C | A | 0.0668508560876641 | 0.2664 | 10 | 23,199,653 | 0.0147685360696215 | 5.83728749218732e−06 | 11,987 | |
| rs4753063 | G | A | −0.059629704 | 0.4414 | 11 | 92,267,129 | 0.013005185305256 | 4.85684299965574e−06 | 11,989 | |
| rs517089 | T | A | 0.0762270290045015 | 0.169 | 11 | 10,967,286 | 0.0170411079985235 | 5.13633569330786e−06 | 11,988 | |
| rs75416973 | A | G | 0.0727223037990453 | 0.2256 | 1 | 172,809,633 | 0.0164509834407607 | 9.46091051490038e−06 | 11,402 | |
| rs764177 | C | A | −0.060145359 | 0.338 | 3 | 45,809,449 | 0.0135365874925541 | 9.60771952878831e−06 | 11,988 | |
| rs76680460 | G | A | −0.16129611 | 0.0477 | 9 | 25,447,080 | 0.0364332108416825 | 8.19213146457629e−06 | 7176 | |
| Eubacterium brachy group | rs112617308 | T | C | −0.170873361 | 0.0795 | 10 | 94,185,213 | 0.0362843970742838 | 2.38032542462856e−06 | 4001 |
| rs12151423 | A | G | 0.101328627102251 | 0.4791 | 2 | 218,237,281 | 0.0227324448974775 | 9.27049702937019e−06 | 3854 | |
| rs13139592 | T | C | −0.145996616 | 0.1223 | 4 | 144,665,178 | 0.0327192091257308 | 7.96753015601087e−06 | 3842 | |
| rs2913110 | C | T | 0.105143062329495 | 0.3777 | 10 | 22,892,667 | 0.0229419728069847 | 4.55805925779257e−06 | 4001 | |
| rs4862235 | G | A | 0.104806298434417 | 0.4443 | 4 | 184,628,931 | 0.0225751345578332 | 3.73143284193457e−06 | 4000 | |
| rs62348779 | T | C | −0.201480569 | 0.0815 | 5 | 17,460,096 | 0.0432860149871789 | 3.78122011636165e−06 | 3827 | |
| rs720439 | A | G | −0.111927002 | 0.2515 | 22 | 48,190,598 | 0.0251252452187253 | 7.02826590315296e−06 | 4001 | |
| rs73199919 | T | C | −0.236716837 | 0.0567 | 4 | 6,856,564 | 0.0531334854965897 | 8.15524120463864e−06 | 3008 | |
| rs9613196 | T | A | −0.238718472 | 0.0765 | 22 | 26,922,534 | 0.0528561035322752 | 4.98786317589988e−06 | 3004 | |
| Eubacterium oxidoreducens group | rs12129908 | C | A | 0.089327250313422 | 0.4473 | 1 | 195,064,019 | 0.0198332870637544 | 5.79515993879262e−06 | 5377 |
| rs12423772 | G | T | 0.140979641070221 | 0.1501 | 12 | 94,515,340 | 0.0295028890109047 | 2.62925374831098e−06 | 5321 | |
| rs1425962 | G | C | 0.0906103936292111 | 0.3429 | 4 | 187,905,399 | 0.0200612323796081 | 7.32284483432928e−06 | 5321 | |
| rs2973294 | G | T | 0.0923558710698249 | 0.4205 | 4 | 37,526,679 | 0.0195438711807532 | 2.38836425360718e−06 | 5378 | |
| rs34561138 | G | A | 0.21614534542657 | 0.0586 | 16 | 86,480,355 | 0.0459919696239924 | 2.51162685546062e−06 | 4186 | |
| Lachnospira | rs13157098 | A | G | −0.076805837 | 0.167 | 5 | 176,802,250 | 0.0155312031423762 | 5.99315187911124e−07 | 15,947 |
| rs159484 | G | A | 0.0794668173228284 | 0.1133 | 4 | 111,995,627 | 0.0176998720426855 | 6.6762236530976e−06 | 16,487 | |
| rs2326833 | C | G | −0.078495035 | 0.1252 | 6 | 6,893,668 | 0.0171090154571239 | 4.59647719901757e−06 | 15,632 | |
| rs2520509 | A | G | 0.0519062566918432 | 0.3181 | 12 | 91,006,514 | 0.0115779593046374 | 7.41854042489015e−06 | 16,500 | |
| rs4686798 | T | C | 0.053182604749454 | 0.3857 | 3 | 186,445,436 | 0.0113747429052122 | 2.73935520427912e−06 | 16,497 | |
| rs4923324 | G | A | −0.061732096 | 0.2386 | 11 | 25,962,890 | 0.0133386567289649 | 2.43592844531939e−06 | 16,490 | |
| rs56791201 | T | C | 0.0518229928563788 | 0.4573 | 2 | 57,701,660 | 0.0110696730729901 | 2.9282274545617e−06 | 16,486 | |
| Parabacteroides | rs114567323 | T | C | 0.18647778419286 | 0.0467 | 3 | 52,354,847 | 0.0405199361255589 | 5.65487090855037e−06 | 5859 |
| rs115602804 | G | A | 0.103080129925596 | 0.0666 | 3 | 191,572,365 | 0.0222735796039486 | 1.93059413977749e−06 | 16,394 | |
| rs3860755 | G | C | 0.0558783809150391 | 0.3082 | 5 | 169,360,155 | 0.0116624537672989 | 1.71324788967126e−06 | 17,294 | |
| rs4236095 | G | A | 0.0761960851351287 | 0.1252 | 6 | 47,368,687 | 0.015704150896379 | 1.93145255048949e−06 | 16,383 | |
| rs60884758 | C | T | −0.07026666 | 0.165 | 9 | 2,217,340 | 0.01422488437102 | 5.70689278570671e−07 | 17,293 | |
| rs6657302 | T | C | −0.104519671 | 0.0517 | 1 | 85,585,609 | 0.022552115930347 | 9.75699485730526e−06 | 16,552 | |
| rs72893646 | A | T | −0.071559422 | 0.1282 | 2 | 38,037,203 | 0.0156667765507473 | 8.83391469071427e−06 | 16,508 | |
| rs7298818 | C | T | 0.0888831512894161 | 0.0765 | 12 | 56,396,953 | 0.0200901897515793 | 8.53629285674445e−06 | 16,510 | |
| Ruminococcaceae UCG002 | rs10927423 | C | A | −0.071361729 | 0.1829 | 1 | 14,732,458 | 0.0147712239567537 | 8.49518084738922e−07 | 17,096 |
| rs10964441 | G | A | −0.149060086 | 0.0527 | 9 | 20,131,746 | 0.0344859968762916 | 7.45115628565522e−06 | 5346 | |
| rs113147300 | A | G | −0.075841986 | 0.1322 | 9 | 114,066,670 | 0.0164562117926448 | 7.69140005682395e−06 | 16,370 | |
| rs11607472 | A | G | −0.0780229 | 0.1074 | 11 | 43,344,751 | 0.0176324837217706 | 7.1867148288996e−06 | 16,282 | |
| rs116974815 | C | A | −0.189730541 | 0.0577 | 11 | 111,712,942 | 0.0396566249883464 | 2.02517837368591e−06 | 5819 | |
| rs11750293 | G | T | −0.057830385 | 0.2813 | 5 | 123,822,114 | 0.0120512292173473 | 1.76136449416771e−06 | 17,094 | |
| rs12463378 | A | G | −0.052206066 | 0.4175 | 19 | 54,475,808 | 0.0112086489902471 | 2.95523248001466e−06 | 16,994 | |
| rs15256 | C | T | 0.0732375870072843 | 0.0865 | 10 | 73,820,548 | 0.0168339965754852 | 9.45658879437907e−06 | 17,095 | |
| rs362417 | G | C | −0.054862993 | 0.2664 | 14 | 73,589,896 | 0.0120983902972034 | 7.80005004827539e−06 | 17,097 | |
| rs55793120 | T | C | 0.137396058695915 | 0.0507 | 12 | 47,384,118 | 0.0274139624198244 | 4.80673981128504e−07 | 11,669 | |
| rs56030423 | G | A | −0.098287388 | 0.0885 | 15 | 90,780,618 | 0.0216344259805208 | 6.30384885218762e−06 | 14,937 | |
| rs57079348 | T | G | −0.07657295 | 0.1213 | 13 | 87,920,450 | 0.0172819496892953 | 7.21821778098982e−06 | 16,372 | |
| rs6542556 | A | G | 0.050974013546106 | 0.3449 | 2 | 120,757,853 | 0.0114060211763915 | 7.86123170788834e−06 | 17,094 | |
| rs6793778 | C | T | −0.055870572 | 0.2624 | 3 | 24,045,453 | 0.0125257938446456 | 9.81288621748968e−06 | 17,094 | |
| rs7155595 | C | A | 0.0569929175820915 | 0.3091 | 14 | 77,502,546 | 0.0116986358144873 | 1.14930695958647e−06 | 17,084 | |
| rs7249614 | A | G | −0.049277826 | 0.3847 | 19 | 43,637,890 | 0.0110806785352943 | 9.07376364258269e−06 | 16,651 | |
| rs72874194 | G | C | −0.077028853 | 0.0994 | 2 | 45,178,830 | 0.0167249537993047 | 3.46664249665733e−06 | 16,651 | |
| rs7342369 | C | A | −0.052780292 | 0.3231 | 12 | 129,451,330 | 0.011602331590505 | 5.66121279947288e−06 | 16,651 | |
| rs76847269 | A | G | 0.163508016675088 | 0.0567 | 5 | 141,014,951 | 0.035615077325021 | 5.17190137948126e−06 | 7654 | |
| rs77564310 | A | C | −0.071309265 | 0.1789 | 15 | 80,713,184 | 0.0140855252513159 | 3.28613761996841e−07 | 17,000 | |
| rs79016051 | C | T | −0.088774672 | 0.0905 | 1 | 238,938,497 | 0.0189424185305035 | 2.33742400302447e−06 | 16,937 | |
| rs882348 | A | G | −0.079994585 | 0.1064 | 4 | 23,325,040 | 0.0178618025783191 | 5.44852168659134e−06 | 17,097 | |
| Ruminococcaceae UCG011 | rs10274562 | C | T | 0.110917239737785 | 0.3519 | 7 | 11,222,521 | 0.0244555510151489 | 6.4960668019854e−06 | 3632 |
| rs12636310 | G | A | 0.13272538065964 | 0.2177 | 3 | 185,469,491 | 0.0282036538976841 | 2.81235442087039e−06 | 3632 | |
| rs12724320 | C | T | −0.120880657 | 0.2753 | 1 | 179,370,499 | 0.0249229168251434 | 1.51986105809649e−06 | 3632 | |
| rs1416041 | A | C | −0.182339224 | 0.1342 | 6 | 105,073,676 | 0.0339898163971193 | 7.04419354623744e−08 | 3632 | |
| rs2729556 | C | T | −0.109096996 | 0.499 | 7 | 111,763,988 | 0.0233698890248395 | 3.18679087269013e−06 | 3632 | |
| rs4490371 | T | C | −0.111816319 | 0.331 | 3 | 76,205,107 | 0.0248962691854784 | 7.74624355444281e−06 | 3610 | |
| rs79113084 | C | T | −0.152165548 | 0.1551 | 12 | 29,634,919 | 0.0317519317337698 | 2.06123459222658e−06 | 3610 | |
| rs9729514 | A | G | 0.184933530444742 | 0.1024 | 1 | 219,901,055 | 0.0394616356047111 | 2.36769356071919e−06 | 3632 | |
| Ruminococcus gauvreauii group | rs12539819 | C | T | 0.11065353642163 | 0.0954 | 7 | 153,487,396 | 0.0240683599559505 | 4.4937474613659e−06 | 11,860 |
| rs13188803 | T | A | 0.0709418725946552 | 0.1879 | 5 | 112,627,773 | 0.0156855194425569 | 7.2755860072598e−06 | 12,834 | |
| rs2047242 | A | G | −0.06760301 | 0.2972 | 10 | 29,201,860 | 0.0133737733507791 | 2.46372145342276e−07 | 13,369 | |
| rs2166943 | A | C | 0.0566973791291385 | 0.4384 | 8 | 137,090,850 | 0.0123496728879881 | 5.2771748011997e−06 | 13,377 | |
| rs289410 | G | A | −0.065492577 | 0.2684 | 15 | 85,563,483 | 0.0139101039891184 | 2.27463597109071e−06 | 13,380 | |
| rs431418 | A | G | −0.094737043 | 0.1193 | 5 | 166,419,552 | 0.0210157100993473 | 5.53667145088742e−06 | 13,289 | |
| rs71386687 | T | G | 0.121036907829049 | 0.0596 | 16 | 2,767,894 | 0.0238599603360403 | 2.90760817470702e−07 | 11,340 | |
| rs73802842 | C | A | 0.0736808103380828 | 0.1431 | 4 | 18,824,860 | 0.016966478373626 | 7.48025669831334e−06 | 13,382 | |
| rs9870933 | A | G | 0.0621642789517628 | 0.4085 | 3 | 112,372,317 | 0.0126071626850593 | 8.49119796265021e−07 | 13,382 |
EA = effect allele, EAF = effect allele frequency, IF = intestinal flora, IgG = immunoglobulin G, OA = other allele, SE = standard error, SNP = single nucleotide polymorphism.
Figure 2.
Forest plot of MR analysis in 11 gut microbiota and IgG levels after H pylori infection. IgG = immunoglobulin G, MR = Mendelian randomization.
There was no evidence of heterogeneity among the genetic IVs for these microbial taxa. This analysis provides insight into the potential causal relationships between gut microbiota and IgG levels, emphasizing the protective or adverse roles of specific bacterial taxa in immune regulation. These results reveal a complex interaction between various gut microbiota and the immune response to H pylori, with certain bacteria possibly playing key roles in modulating the immune system. Our sensitivity analyses, including MR-Egger intercept, leave-one-out, and weighted median methods, supported the robustness of these findings, showing no single SNP disproportionately influenced the outcomes, and no evidence of horizontal pleiotropy was observed (all intercepts P > .05). This analysis provides a clearer understanding of how specific gut microbiota can impact immune responses post-infection, offering potential targets for modulating these effects in therapeutic interventions (see Figures 3 and 4).
Figure 3.
Scatter plot of sensitivity analysis in 11 gut microbiota and IgG levels after H pylori infection. (A) Ruminococcus gauvreauii group, (B) Parabacteroides, (C) Lachnospira, (D) Eubacterium oxidoreducens group, (E) Ruminococcaceae UCG011, (F) Ruminococcaceae UCG002, (G) Eubacterium brachy group, (H) Dialister, (I) Candidatus Soleaferrea, (J) Anaerofilum, (K) Akkermansia.. IgG = immunoglobulin G.
Figure 4.
Leave-one-out analysis of 11 gut microbiota and IgG levels after H pylori infection. (A) Ruminococcaceae UCG011, (B) Ruminococcaceae UCG002, (C) Parabacteroides, (D) Lachnospira, (E) Eubacterium oxidoreducens group, (F) Eubacterium brachy group, (G) Dialister, (H) Candidatus Soleaferrea, (I) Anaerofilum, (J) Akkermansia, (K) Ruminococcus gauvreauii group. IgG = immunoglobulin G.
3.2. Gene and function analysis
Detailed mappings between SNPs and genes are outlined in Table 3. GO analysis showed that gut microbiota associated with unfavorable outcomes might be involved in the following activities such as neurotransmitter receptor transport/regulation of AMPA receptor activity/postsynaptic density assembly/inner dynein arm assembly/regulation of postsynaptic density organization/regulation of postsynaptic specialization assembly/cell-cell adhesion via plasma-membrane adhesion molecules/AMPA glutamate receptor complex/axonemal dynein complex/dynein complex/neurotransmitter receptor complex/ionotropic glutamate receptor complex/plasma-membrane bounded cell projection cytoplasm/actin-based cell projection/neuron to neuron synapse/postsynaptic membrane/synaptic membrane/microtubule motor activity/amino acid binding/dynein intermediate chain binding/dynein light intermediate chain binding/minus-end-directed microtubule motor activity/guanyl-nucleotide exchange factor activity/serine hydrolase activity/serine-type peptidase activity/calcium ion transmembrane transporter activity/protein heterodimerization activity. Favorable processes identified include retrograde axonal transport/regulation of postsynaptic density assembl. KEGG pathway analysis highlighted significant pathways including sulfur metabolism/inflammatory mediator regulation of TRP channels/motor proteins (see Fig. 5).
Table 3.
Significant SNP adjacent gene conversion.
| ID | Chr | Start | End | Strand | Gene_IDs | Gene_Names |
|---|---|---|---|---|---|---|
| rs111862613 | 12 | 129,825,125 | 129,825,125 | + | ENSG00000151952 | TMEM132D |
| rs2602429 | 16 | 81,029,544 | 81,029,544 | + | ENSG00000166451, ENSG00000260213, ENSG00000284512 | CENPN, CENPN-AS1, ENSG00000284512 |
| rs4936098 | 11 | 130,410,772 | 130,410,772 | + | ENSG00000134917 | ADAMTS8 |
| rs74542928 | 4 | 99,623,031 | 99,623,031 | + | ENSG00000138823, ENSG00000248676 | MTTP, ENSG00000248676 |
| rs9349825 | 6 | 56,476,683 | 56,476,683 | + | ENSG00000151914 | DST |
| rs941682 | 20 | 33,280,034 | 33,280,034 | + | ENSG00000125999 | BPIFB1 |
| rs10794359 | 11 | 1,051,715 | 1,051,715 | + | ENSG00000254872 | LINC02688 |
| rs17105491 | 10 | 123,534,890 | 123,534,890 | + | ENSG00000230131 | LINC02641 |
| rs4244069 | 12 | 66,773,681 | 66,773,681 | + | ENSG00000155974 | GRIP1 |
| rs712981 | 3 | 129,967,591 | 129,967,591 | + | ENSG00000250643 | ENSG00000250643 |
| rs79598899 | 2 | 190,274,425 | 190,274,425 | + | ENSG00000198130 | HIBCH |
| rs9299345 | 9 | 101,577,530 | 101,577,530 | + | ENSG00000198785 | GRIN3A |
| rs10090365 | 8 | 137,625,956 | 137,625,956 | + | ENSG00000285817 | ENSG00000285817 |
| rs10108780 | 8 | 122,870,594 | 122,870,594 | + | ENSG00000178764 | ZHX2 |
| rs10809135 | 9 | 10,611,282 | 10,611,282 | + | ENSG00000153707 | PTPRD |
| rs11153159 | 6 | 109,055,444 | 109,055,444 | + | ENSG00000080546 | SESN1 |
| rs2193878 | 3 | 190,631,260 | 190,631,260 | + | ENSG00000196083 | IL1RAP |
| rs4294381 | 1 | 224,500,405 | 224,500,405 | + | ENSG00000143786 | CNIH3 |
| rs4678258 | 3 | 138,226,350 | 138,226,350 | + | ENSG00000114098 | ARMC8 |
| rs7400877 | 14 | 75,937,199 | 75,937,199 | + | ENSG00000119650, ENSG00000119685 | IFT43, TTLL5 |
| rs830149 | 19 | 47,488,184 | 47,488,184 | + | ENSG00000105402, ENSG00000268061, ENSG00000279861 | NAPA, NAPA-AS1, ENSG00000279861 |
| rs10138457 | 14 | 101,991,715 | 101,991,715 | + | ENSG00000197102 | DYNC1H1 |
| rs11071887 | 15 | 32,662,894 | 32,662,894 | + | ENSG00000166922, ENSG00000241818, ENSG00000288864 | SCG5, SCG5-AS1, ARHGAP11A-SCG5 |
| rs2435610 | 7 | 151,192,947 | 151,192,947 | + | ENSG00000278685 | IQCA1L |
| rs4753063 | 11 | 92,533,963 | 92,533,963 | + | ENSG00000165323 | FAT3 |
| rs75416973 | 1 | 172,840,493 | 172,840,493 | + | ENSG00000224228 | ENSG00000224228 |
| rs764177 | 3 | 45,767,957 | 45,767,957 | + | ENSG00000163817 | SLC6A20 |
| rs112617308 | 10 | 92,425,456 | 92,425,456 | + | ENSG00000286795 | ENSG00000286795 |
| rs12151423 | 2 | 217,372,558 | 217,372,558 | + | ENSG00000231672 | DIRC3 |
| rs13139592 | 4 | 143,744,025 | 143,744,025 | + | ENSG00000251600 | GUSBP5 |
| rs2913110 | 10 | 22,603,738 | 22,603,738 | + | ENSG00000150867 | PIP4K2A |
| rs4862235 | 4 | 183,707,778 | 183,707,778 | + | ENSG00000168538 | TRAPPC11 |
| rs62348779 | 5 | 17,459,987 | 17,459,987 | + | ENSG00000249662 | LINC02218 |
| rs720439 | 22 | 47,794,849 | 47,794,849 | + | ENSG00000224271 | EPIC1 |
| rs73199919 | 4 | 6,854,837 | 6,854,837 | + | ENSG00000170871 | KIAA0232 |
| rs9613196 | 22 | 26,526,568 | 26,526,568 | + | ENSG00000128294 | TPST2 |
| rs1425962 | 4 | 186,984,245 | 186,984,245 | + | ENSG00000249742 | ENSG00000249742 |
| rs2973294 | 4 | 37,525,057 | 37,525,057 | + | ENSG00000154274 | C4orf19 |
| rs159484 | 4 | 111,074,471 | 111,074,471 | + | ENSG00000288692 | ENSG00000288692 |
| rs2520509 | 12 | 90,612,737 | 90,612,737 | + | ENSG00000286021 | LINC02822 |
| rs4686798 | 3 | 186,727,647 | 186,727,647 | + | ENSG00000113889, ENSG00000197099 | KNG1, HRG-AS1 |
| rs114567323 | 3 | 52,320,831 | 52,320,831 | + | ENSG00000114841 | DNAH1 |
| rs3860755 | 5 | 169,933,151 | 169,933,151 | + | ENSG00000134516, ENSG00000204767 | DOCK2, INSYN2B |
| rs6657302 | 1 | 85,119,926 | 85,119,926 | + | ENSG00000162643 | DNAI3 |
| rs7298818 | 12 | 56,003,169 | 56,003,169 | + | ENSG00000139531 | SUOX |
| rs10927423 | 1 | 14,405,962 | 14,405,962 | + | ENSG00000189337, ENSG00000234593 | KAZN, KAZN-AS1 |
| rs11607472 | 11 | 43,323,201 | 43,323,201 | + | ENSG00000166181 | API5 |
| rs116974815 | 11 | 111,842,219 | 111,842,219 | + | ENSG00000086848, ENSG00000258529 | ALG9, ENSG00000258529 |
| rs12463378 | 19 | 53,972,554 | 53,972,554 | + | ENSG00000142408 | CACNG8 |
| rs15256 | 10 | 72,060,790 | 72,060,790 | + | ENSG00000107742 | SPOCK2 |
| rs362417 | 14 | 73,123,188 | 73,123,188 | + | ENSG00000119707 | RBM25 |
| rs56030423 | 15 | 90,237,386 | 90,237,386 | + | ENSG00000183208, ENSG00000284626 | GDPGP1, ENSG00000284626 |
| rs7155595 | 14 | 77,036,203 | 77,036,203 | + | ENSG00000289347 | ENSG00000289347 |
| rs7249614 | 19 | 43,133,738 | 43,133,738 | + | ENSG00000282943 | PSG11-AS1 |
| rs7342369 | 12 | 128,966,785 | 128,966,785 | + | ENSG00000151948 | GLT1D1 |
| rs76847269 | 5 | 141,635,384 | 141,635,384 | + | ENSG00000171720 | HDAC3 |
| rs77564310 | 15 | 80,420,842 | 80,420,842 | + | ENSG00000172379 | ARNT2 |
| rs10274562 | 7 | 11,182,894 | 11,182,894 | + | ENSG00000230333 | ENSG00000230333 |
| rs12636310 | 3 | 185,751,703 | 185,751,703 | + | ENSG00000073792 | IGF2BP2 |
| rs12724320 | 1 | 179,401,364 | 179,401,364 | + | ENSG00000162779 | AXDND1 |
| rs2729556 | 7 | 112,123,933 | 112,123,933 | + | ENSG00000128512 | DOCK4 |
| rs4490371 | 3 | 76,155,956 | 76,155,956 | + | ENSG00000185008 | ROBO2 |
| rs79113084 | 12 | 29,481,986 | 29,481,986 | + | ENSG00000187950, ENSG00000257599 | OVCH1, OVCH1-AS1 |
| rs9729514 | 1 | 219,727,713 | 219,727,713 | + | ENSG00000196660 | SLC30A10 |
| rs12539819 | 7 | 153,790,311 | 153,790,311 | + | ENSG00000130226 | DPP6 |
| rs13188803 | 5 | 113,292,076 | 113,292,076 | + | ENSG00000171444 | MCC |
| rs2047242 | 10 | 28,912,931 | 28,912,931 | + | ENSG00000232624 | LINC01517 |
| rs2166943 | 8 | 136,078,607 | 136,078,607 | + | ENSG00000254101 | LINC02055 |
| rs289410 | 15 | 85,020,252 | 85,020,252 | + | ENSG00000073417 | PDE8A |
| rs431418 | 5 | 166,992,547 | 166,992,547 | + | ENSG00000145934 | TENM2 |
| rs71386687 | 16 | 2,717,893 | 2,717,893 | + | ENSG00000172382 | PRSS27 |
| rs73802842 | 4 | 18,823,237 | 18,823,237 | + | ENSG00000286046 | ENSG00000286046 |
SNP = single nucleotide polymorphism.
Figure 5.
KEGG and GO enrichment analysis of adjacent genes. (A) GO enrichment analysis, (B) KEGG enrichment analysis. GO = gene ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.
4. Discussion
4.1. Principal findings
This study provides novel insights into the causal relationship between gut microbiota composition and IgG levels following H pylori infection, highlighting the dual roles of specific microbial taxa. Protective taxa such as Akkermansia and Ruminococcaceae UCG002 were associated with reduced IgG levels, whereas taxa like Parabacteroides and Dialister exhibited adverse effects by increasing IgG levels. These findings were robustly supported by sensitivity analyses, demonstrating no evidence of pleiotropy or heterogeneity. Functional enrichment analysis further suggested that the identified taxa influence IgG levels through pathways involved in immune regulation, neurotransmitter signaling, and inflammatory responses, offering mechanistic insights into microbiota-mediated immune modulation.
4.2. Integration with existing literature
IgG is the primary antibody isotype in serum, playing a critical role in combating infections, neutralizing toxins, and eliminating pathogens.[32] The gut microbiota and host immune system maintain ongoing crosstalk, which is essential for the maturation and function of the immune system.[33] Our findings align with and expand upon existing literature that underscores the critical role of gut microbiota in modulating immune responses. Prior studies have identified significant alterations in microbial diversity and composition following H pylori infection.[34] Our study reveals that increased abundances of 5 gut microbial taxa – Eubacterium oxidoreducens group, Ruminococcaceae UCG002, Anaerofilum, Akkermansia, Ruminococcaceae UCG011, and Candidatus Soleaferrea—exhibit a negative causal relationship with IgG level changes. Specifically, higher abundances of these 6 taxa correlate with more stable IgG levels, suggesting that a more stable immune state serves as a relatively protective factor following H pylori infection. Consistent with prior research, we found that these 6 taxa regulate immune homeostasis through pathways independent of IgG, antagonizing H pylori to exert protective effects. Taxa such as Ruminococcaceae and Eubacteriaceae produce butyrate, a SCFA that not only directly kills pathogens by disrupting cell envelope integrity[35] but also acts as an “anti-virulence” agent by downregulating the expression of key virulence factors like CagA, VacA and inhibiting urease activity, thereby weakening H pylori’s pathogenicity and survival capacity.[34,36,37] Akkermansia muciniphila strengthens the physical barrier by degrading mucins to stimulate their re-secretion, thickening the mucus layer,[38,39] and upregulating tight junction protein expression to reinforce epithelial integrity,[39] which blocks pathogen adhesion and penetration. Meanwhile, its metabolites acetate and propionate support butyrate-producing taxa via “cross-feeding,” creating metabolic synergy.[40,41] These SCFAs—particularly butyrate—act as histone deacetylase inhibitors and G protein-coupled receptor ligands,[42,43] effectively suppressing excessive inflammatory pathways such as NF-κB induced by H pylori[37] and promoting regulatory T cell function. This limits immunopathological damage and disrupts the inflammatory microenvironment upon which pathogens depend for survival.[44] A healthy, diverse microbiome represented by taxa such as Anaerofilum and Candidatus Soleaferrea restricts H pylori colonization space fundamentally through niche occupation, nutrient depletion,[45] and maintenance of an anaerobic environment,[44–46] thereby establishing an intrinsically resilient gut homeostasis. Studies have reported that the aforementioned gut microbiota may maintain immune homeostasis and antagonize H pylori infection, which is consistent with our experimental results.
Our study demonstrates that the abundances of 5 gut microbial taxa—Dialister, Parabacteroides, Lachnospira, Ruminococcus gauvreauii group, and Eubacterium brachy group—are significantly and causally associated with changes in serum IgG levels in H pylori-infected patients, exerting detrimental effects. Consistent with prior reports, 1 study revealed that exogenous antigens from Eubacterium brachy are recognized by APCs, activating T cell-dependent or -independent B cell responses and inducing IgG production in human serum.[47] Membrane-bound lipopolysaccharide (LPS) of Parabacteroides, a classical B cell mitogen and immune activator, likely promotes B cell proliferation and antibody secretion nonspecifically via pattern recognition receptor pathways such as TLR4. Notably, a direct correlation was demonstrated between Parabacteroides abundance and serum levels of IgG, IgG1, IgG2a, and IgG2b.[48] Evidence suggests Lachnospiraceae may stimulate immunoglobulin production by expressing B cell superantigens.[49] Within H pylori-infected contexts, IgG alterations driven by these taxa may exacerbate gastric inflammation and infection progression.[50] By intensifying immune imbalance and chronic inflammation, these communities form abundant local immune complexes in H pylori-infected gastric mucosa, activating the complement system, recruiting/activating immune cells, and releasing like IL-6, TNF-α and ROS—ultimately amplifying local tissue injury.[51,52] Dialister pneumosintes has been linked to gastric cancer progression,[53] and Dialister enrichment correlates negatively with improved IL-6 levels.[54] Additionally, as Gram-negative bacteria, Parabacteroides and Dialister activate key inflammatory signaling pathways, driving robust expression of downstream pro-inflammatory cytokines like IL-6 and IL-8.[55–57] The aforementioned gut microbiota have been confirmed in recent reports to play a negative role in resisting H pylori infection, consistent with our findings.
4.3. Mechanistic insights: functional enrichment analysis
In addition to confirming known mechanisms, this study identifies new associations that extend the understanding of microbiota-mediated immune regulation. We analyzed the genes related to the influence of gut microbiota on anti-H pylori IgG levels and predicted potential mechanisms through GO and KEGG enrichment analyses. GO enrichment analysis suggests that cell-cell adhesion via plasma-membrane adhesion molecules is a critical process. Cell-cell adhesion via plasma-membrane adhesion molecules participates in the processes of H pylori infection and host defense. These adhesion mechanisms are essential for maintaining tissue integrity but are significantly impacted during H pylori colonization, a critical step in the bacterium’s pathogenesis. Key adhesion molecules, such as BabA and SabA, enable H pylori to bind to gastric epithelial cells, while integrins like α5 and β1 further facilitate adhesion and subsequent CagA protein translocation. The infection disrupts cell-cell adhesion, alters adhesion molecule expression, and activates signaling pathways, such as MAPK, that increase matrix metalloproteinase expression. Therapeutic approaches targeting these mechanisms, including antiadhesion compounds, antioxidants, and nanoparticle-based drug delivery, show promise in mitigating H pylori-induced damage.[58–63] KEGG analysis suggests that motor proteins may play a key role in the interaction between gut microbiota and H pylori. Motor proteins play a crucial role in H pylori infection and its pathogenicity. They drive flagellar motility, enabling the bacteria to penetrate the gastric mucus layer and colonize the stomach lining. Structural proteins like FlgV are essential for assembling high-torque flagellar motors, while outer membrane proteins enhance bacterial adhesion to host cells.[64,65] Although their role in infection is well-studied, targeting motor proteins may offer potential strategies for combating H pylori infections.
4.4. Clinical implications
The clinical implications of these findings are significant. Modulating gut microbiota could offer a promising adjunctive strategy for managing H pylori-associated immune dysregulation. For instance, probiotics targeting beneficial taxa such as reducing the Lachnospira might enhance immune balance and mitigate inflammatory responses, while interventions to suppress adverse taxa could reduce excessive IgG levels. Based on the results of GO and KEGG enrichment analyses, future research could explore the molecular mechanisms by which beneficial microbiota enhance the host’s immunity against H pylori through cell-cell adhesion via plasma-membrane adhesion molecules and motor proteins. Additionally, therapies aimed at restoring microbial diversity disrupted by antibiotics or eradication therapy could have long-term benefits for immune health. Future clinical studies are necessary to evaluate the efficacy of such microbiota-based interventions in improving immune outcomes following H pylori infection.
4.5. Strengths and limitations
This study has several notable strengths. First, to our knowledge, it is the first to apply a comprehensive MR framework to investigate the causal impact of gut microbiota composition on anti-H pylori IgG levels. This design reduces bias from confounding and reverse causation, which commonly affect traditional observational studies. Second, the robustness of our results was confirmed through multiple sensitivity analyses, including MR-Egger, weighted median, MR-PRESSO, and leave-one-out analyses, with no evidence of horizontal pleiotropy or heterogeneity. Third, by integrating gene colocalization and functional enrichment (GO and KEGG) analyses, we explored the potential biological mechanisms underlying the observed associations, adding mechanistic depth to our findings.
Nevertheless, this study has several limitations. The use of GWAS summary statistics primarily derived from European populations may limit the generalizability of these findings to other populations with different genetic and microbial compositions. Furthermore, the cross-sectional nature of the data precludes a comprehensive understanding of the temporal dynamics of gut microbiota and IgG level changes. Longitudinal studies incorporating high-resolution multi-omic approaches, such as single-cell transcriptomics and metabolomics, are essential to further elucidate the causal mechanisms underlying these relationships.
5. Conclusion
In summary, our research has established a causal link between gut microbiota and the development of IgG levels after H pylori infection. These specific bacterial strains could serve as novel biomarkers and offer a multitude of potential therapeutic targets for managing or preventing IgG levels after H pylori infection. We observed that 6 gut microbial taxa, including Akkermansia and Ruminococcaceae UCG002, exhibit a negative causal association with IgG level changes, exerting beneficial effects. Conversely, 5 genera such as Parabacteroides and Dialister display a significant positive causal relationship with IgG level alterations, potentially inducing excessive activation or suppression of the immune system and leading to detrimental consequences. Moreover, there is an urgent need for a more holistic approach that incorporates diverse omics data to enhance our understanding of IgG levels after H pylori infection, especially considering the complex interplay between genetic and environmental factors over time. At present, the specific mechanism of anti-H pylori IgG treatment for H pylori is still unclear, as well as the mechanism of the influence of intestinal flora on IgG. It is hoped that research can be conducted in related directions in the future.
Acknowledgments
We appreciate the support provided by the National Natural Science Foundation of China under Grant No. 81,874,426, the China Postdoctoral Science Foundation (Special Fund for Postdoctoral Research in the 15th Batch) under Grant No. 2022T150069, the National College Students Innovation and Entrepreneurship Training Program (National Key Support Areas) under Grant No. 202310228063, the National College Students Innovation and Entrepreneurship Training Program (National General Projects) under Grant No. 202410228015, the College Students Innovation and Entrepreneurship Training Program (Provincial Guidance) under Grant No. S202310228002, the College Students Innovation and Entrepreneurship Training Program of Beijing University of Chinese Medicine (Municipal Level) under Grant No. S202410026025, the Scientific and Technological Innovation Program of Heilongjiang University of Chinese Medicine under Grant No. KY2022-04, and the Beijing University of Chinese Medicine Postgraduate-to-Doctoral Research Project under Grant No. XBB24061.
Author contributions
Conceptualization: Qiang Fu, Yin Fu.
Data curation: Junlei Chen.
Formal analysis: Tunan Ding.
Funding acquisition: Tunan Ding, Qiang Fu.
Investigation: Xiaojie Zhou.
Methodology: Tunan Ding, Yang Liu.
Project administration: Yin Fu.
Software: Tunan Ding.
Supervision: Qiang Fu, Yin Fu.
Validation: Yilin Li.
Writing – original draft: Junlei Chen.
Writing – review & editing: Xiaojie Zhou, Yin Fu.
Abbreviations:
- CagA
- cytotoxin-associated gene A
- CI
- confidence interval
- EAF
- effect allele frequency
- eQTL
- expression quantitative trait loci
- FDR
- false discovery rate
- GO
- gene ontology
- GWAS
- genome-wide association study
- H pylori =
- Helicobacter pylori
- IgG
- immunoglobulin G
- IL-6/8
- interleukin-6/8
- IV
- instrumental variable
- IVW
- inverse variance-weighted
- KEGG
- Kyoto Encyclopedia of Genes and Genomes
- LD
- linkage disequilibrium
- MAPK
- mitogen-activated protein kinase
- MR
- Mendelian randomization
- MR-PRESSO
- Mendelian randomization pleiotropy residual sum and outlier
- MUC5AC
- mucin 5AC
- OMP
- outer membrane protein
- OR
- odds ratio
- PPI
- proton pump inhibitor
- SCFA
- short-chain fatty acid
- SNP
- single nucleotide polymorphism
- STING
- stimulator of interferon genes
- TLR
- Toll-like receptor
- TNF-α
- tumor necrosis factor-alpha
- Treg
- regulatory T cell
- VacA
- vacuolating cytotoxin A
This study was supported by the National Natural Science Foundation of China under Grant No. 81874426, the China Postdoctoral Science Foundation (Special Fund for Postdoctoral Research in the 15th Batch) under Grant No. 2022T150069, the National College Students Innovation and Entrepreneurship Training Program (National Key Support Areas) under Grant No. 202310228063, the National College Students Innovation and Entrepreneurship Training Program (National General Projects) under Grant No. 202410228015, the College Students Innovation and Entrepreneurship Training Program (Provincial Guidance) under Grant No. S202310228002, the College Students Innovation and Entrepreneurship Training Program of Beijing University of Chinese Medicine (Municipal Level) under Grant No. S202410026025, the Scientific and Technological Innovation Program of Heilongjiang University of Chinese Medicine under Grant No. KY2022-04, the Beijing University of Chinese Medicine Postgraduate-to-Doctoral Research Project under Grant No. XBB24061, the Natural Science Foundation of Heilongjiang Province (No. YQ2024H027), the Heilongjiang Provincial Traditional Chinese Medicine Research Project (No. ZHY2025-015), the Heilongjiang Provincial Undergraduate Universities' “Outstanding Young Teachers” Basic Research Support Program (No. YQJH2024224), the Heilongjiang Provincial Traditional Chinese Medicine Classics Popularization Special Project (No. ZYW2025-007), and the Heilongjiang University of Traditional Chinese Medicine Research Fund Project (Doctoral Innovation Fund) (No. 2019BS05).
Informed consent was not required, as the study utilized publicly available, de-identified summary-level data from public databases.
The data in this article were obtained from the IEU OpenGWAS project. No ethics approval was required.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Chen J, Zhou X, Ding T, Li Y, Liu Y, Fu Q, Fu Y. Causal effects of gut microbiota on IgG levels after Helicobacter pylori (H pylori) infection: Insights from genome-wide Mendelian randomization. Medicine 2025;104:52(e46619).
JC, XZ, and TD contributed equally to this work.
Contributor Information
Junlei Chen, Email: chinstone0416@yeah.net.
Xiaojie Zhou, Email: zhouxj_edu@outlook.com.
Tunan Ding, Email: 18811377192@163.com.
Yilin Li, Email: 3142341767@qq.com.
Yang Liu, Email: liuyang1978@hljucm.edu.cn.
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